Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 Jan 19;10(2):137.
doi: 10.3390/bioengineering10020137.

Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies

Lorenza Bonaldi  1 , Andrea Pretto  2 , Carmelo Pirri  3 , Francesca Uccheddu  2   4 , Chiara Giulia Fontanella  2   4 , Carla Stecco  3   4
Affiliations
Review

Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies

Lorenza Bonaldi et al. Bioengineering (Basel). .

Abstract

By leveraging the recent development of artificial intelligence algorithms, several medical sectors have benefited from using automatic segmentation tools from bioimaging to segment anatomical structures. Segmentation of the musculoskeletal system is key for studying alterations in anatomical tissue and supporting medical interventions. The clinical use of such tools requires an understanding of the proper method for interpreting data and evaluating their performance. The current systematic review aims to present the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and the related strategies utilized by different authors. A search was performed using the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published up until February 2022 were obtained and analyzed according to the PRISMA framework in terms of anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creation, network architecture, loss functions, performance indicators and so on. Several common trends emerged from this survey; however, the different methods need to be compared and discussed based on each specific case study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can be used to guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.

Keywords: CT; MRI; X-ray; anatomical structures; artificial intelligence; deep learning; medical imaging; musculoskeletal system; segmentation; ultrasonography.

PubMed Disclaimer

Conflict of interest statement

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Examples of image transformations, flipping (top) and rotation (bottom).
Figure 2
Figure 2
Image pre-processing: filtering techniques applied to the native image.
Figure 3
Figure 3
Training process with weights update workflow (top); validation/testing process workflow performed on different subsets (bottom).
See this image and copyright information in PMC

References

    1. Wang P., Vives M., Patel V.M., Hacihaliloglu I. Robust Real-Time Bone Surfaces Segmentation from Ultrasound Using a Local Phase Tensor-Guided CNN. Int. J. Comput. Assist. Radiol. Surg. 2020;15:1127–1135. doi: 10.1007/s11548-020-02184-1. - DOI - PubMed
    1. Alsinan A.Z., Patel V.M., Hacihaliloglu I. Automatic Segmentation of Bone Surfaces from Ultrasound Using a Filter-Layer-Guided CNN. Int. J. Comput. Assist. Radiol. Surg. 2019;14:775–783. doi: 10.1007/s11548-019-01934-0. - DOI - PubMed
    1. Kompella G., Antico M., Sasazawa F., Jeevakala S., Ram K., Fontanarosa D., Pandey A.K., Sivaprakasam M. Segmentation of Femoral Cartilage from Knee Ultrasound Images Using Mask R-CNN. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS. 2019;2019:966–969. doi: 10.1109/EMBC.2019.8857645. - DOI - PubMed
    1. Balagopal A., Kazemifar S., Nguyen D., Lin M.H., Hannan R., Owrangi A., Jiang S. Fully Automated Organ Segmentation in Male Pelvic CT Images. Phys. Med. Biol. 2018;63:245015. doi: 10.1088/1361-6560/aaf11c. - DOI - PubMed
    1. Norman B., Pedoia V., Majumdar S. Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry. Radiology. 2018;288:177–185. doi: 10.1148/radiol.2018172322. - DOI - PMC - PubMed

LinkOut - more resources